Unravelling Epigenomic Diversity in Arabidopsis thaliana: Integrating JSD Analysis and `Methylator´ Framework for Climate Adaptation Insights

Eylül Harputluoglu1, 2, 3 , Jürgen Bernard2, Ueli Grossniklaus1,3, Deepak Tanwar1,3


1 Plant Development Genetics, Department of Plant and Microbial Biology, University of Zurich
2 Department of Computer Science, University of Zurich
3 URPP Evolution in Action, Department of Plant and Microbial Biology, University of Zurich

eyluelgoekce.harputluoglu@uzh.ch | deepak.tanwar@evolution.uzh.ch

Introduction

DNA cytosine methylation is the addition of a methyl group to a cytosine in the DNA. It impacts transcription and therefore plays a major role in several vital processes. In mammals, DNA cytosine methylation predominantly occurs in CG sequence contexts. In plants, in addition to the CG context, the CHG and CHH contexts are common as well.


Figure 1: Methylation contexts in mammals and plants.

Genomic regions, which control gene expression and protein production, are influenced by DNA methylation. Methylation can modify how these regions function, often silencing gene expression or regulating gene activity, thus playing a important role in development and genome stability.


Figure 2: Schematic representation of the transcriptional and translational regions of a gene, including the upstream and downstream regions. The figure highlights the intergenic regions, untranslated regions (UTR), exons, and introns.

Methods

Jensen-Shannon Divergence (JSD) measures the similarity between probability distributions, providing a symmetric comparison of datasets. In DNA methylation analysis, JSD can be used to compare methylation patterns across samples, identifying regions with significant epigenetic differences.


Figure 3: Jensen-Shannon Divergence (JSD) formula and entropy calculation.

The goal of smoothing in DNA methylation analysis is to lower mistakes in divergence computations so that methylation patterns can be compared more accurately. This method contributes to sample consistency, which yields more reliable results for analysis such as JSD.


Figure 4: Comparison of various smoothing methods across entropy, JSD, and Kolmogorov-Smirnov metrics.

Datasets

The datasets used in this study are from the [ 1001 Arabidopsis Epigenomes Project] (http://signal.salk.edu/1001.php)

Figure 5: Dataset distribution (top-left) and sample count per country for the 1001 Arabidopsis Epigenomes Project.

Pipeline

This pipeline supports DNA methylation analysis by utilizing divergence calculations and generating outputs that can be used for additional statistical testing or visualization for further analysis.We used the `Methylator´ Framework for the data preparetion.


Figure 6: Overview of the DNA methylation analysis pipeline

Results

We analyzed the impact of different temperature conditions (10°C, 16°C, 22°C) on DNA methylation in the CHG, CHH, and CpG contexts across various genomic regions. By calculating the JSD and methylation levels, we revealed distinct patterns of methylation context-specific changes across these temperature conditions.


Figure 7: Jensen-Shannon Divergence (JSD) as a function of methylation levels in CHG, CHH, and CpG contexts under three temperature conditions.

We further explored how methylation levels and JSD vary across different genomic regions, such as exonic, intergenic, and transposable elements, under three temperature conditions (10°C, 16°C, 22°C). This analysis aimed to identify regions that are particularly sensitive to temperature-induced epigenetic changes. The results could provide a clearer image of how genomic context influences methylation patterns and gene regulation in response to environmental stress.


Figure 8: Heatmap comparison of methylation levels (Meth) and JSD across various genomic regions (exonic, intergenic, intronic, etc.) and temperatures (10°C, 16°C, 22°C) in CHG, CHH, and CpG contexts.


Figure 9: Methylation and JSD patterns in CpG sites across intergenic regions under three temperature conditions. The color gradient indicates the level of methylation and JSD, with red representing higher values and blue representing lower values.

Additional Findings

Different gene expression profiles among samples at different temperatures (10°C, 16°C, and 22°C) are displayed in this transcriptome heatmap. The clustering shows that there are discernible variations in the effects of temperature on gene expression. The precise genes at play and their possible functions in temperature adaptation and stress responses in plants require more research.

Gene Expression Levels Across Temperature Conditions
Figure 10: Transcriptomic heatmap showing gene expression profiles under different temperature conditions (10°C, 16°C, 22°C)

Conclusion

  • Objective: Investigate how temperature affects DNA methylation and gene expression in Arabidopsis thaliana.
  • Methodology: Analyzed methylation levels and Jensen-Shannon Divergence at 10°C, 16°C, and 22°C.
  • Results: Identified temperature-dependent patterns in methylation and gene expression.
  • Implications: Improved understanding of plant epigenetic responses to temperature.
  • Future Directions: Explore specific genes and other environmental factors.